Formation Period Matters
Yanhao Zhang, Lei Hua Qin, Shengping Zhang, Hongxun Yao, Qingming Huang · 2015
Group detection becomes an important task in crowd behavior surveillance. However, most existing methods ignore the formation persistency characteristics, which predict unreliable interactions when the crowd is realistic and complex. To address this issue, we propose a novel graph-based method to declare that the formation period really matters for detecting social groups in crowd. First, we develop a socially motivated representation by modeling the formation period probability in a Bayesian manner, which results in social and temporal consistency for group member interactions. A graph is then established using individuals as nodes and formation periods as edge weights to reflect pedestrian relationships. In this way, seeking of socially consistent groups is converted into an optimization problem which seeks dense subgraphs with maximum formation likelihood within the graph structure. We employ graph shift optimization to detect groups by finding all the dense subgraphs due to its robust performance. In the experimental results on public datasets, our proposed method clearly outperforms other related state-of-the-art methods.